"""Python wrappers around TensorFlow ops.

This file is MACHINE GENERATED! Do not edit.
"""

import collections as _collections
import six as _six

from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
from tensorflow.python.eager import context as _context
from tensorflow.python.eager import core as _core
from tensorflow.python.eager import execute as _execute
from tensorflow.python.framework import dtypes as _dtypes
from tensorflow.python.framework import errors as _errors
from tensorflow.python.framework import tensor_shape as _tensor_shape

from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
# Needed to trigger the call to _set_call_cpp_shape_fn.
from tensorflow.python.framework import common_shapes as _common_shapes
from tensorflow.python.framework import op_def_registry as _op_def_registry
from tensorflow.python.framework import ops as _ops
from tensorflow.python.framework import op_def_library as _op_def_library
from tensorflow.python.util.deprecation import deprecated_endpoints
from tensorflow.python.util import dispatch as _dispatch
from tensorflow.python.util.tf_export import tf_export
from tensorflow.python.util.tf_export import kwarg_only as _kwarg_only
from tensorflow.tools.docs import doc_controls as _doc_controls


def non_deterministic_ints(shape, dtype=_dtypes.int64, name=None):
  r"""Non-deterministically generates some integers.

  This op may use some OS-provided source of non-determinism (e.g. an RNG), so each execution will give different results.

  Args:
    shape: A `Tensor`. The shape of the output tensor.
    dtype: An optional `tf.DType`. Defaults to `tf.int64`.
      The type of the output.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `dtype`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "NonDeterministicInts", name, _ctx._post_execution_callbacks, shape,
        "dtype", dtype)
      return _result
    except _core._FallbackException:
      try:
        return non_deterministic_ints_eager_fallback(
            shape, dtype=dtype, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  if dtype is None:
    dtype = _dtypes.int64
  dtype = _execute.make_type(dtype, "dtype")
  _, _, _op = _op_def_lib._apply_op_helper(
        "NonDeterministicInts", shape=shape, dtype=dtype, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("dtype", _op.get_attr("dtype"), "shape_dtype",
            _op.get_attr("shape_dtype"))
  _execute.record_gradient(
      "NonDeterministicInts", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def NonDeterministicInts(shape, dtype=_dtypes.int64, name=None):
  return non_deterministic_ints(shape=shape, dtype=dtype, name=name)
NonDeterministicInts.__doc__ = non_deterministic_ints.__doc__
NonDeterministicInts = _doc_controls.do_not_generate_docs(_kwarg_only(NonDeterministicInts))
tf_export("raw_ops.NonDeterministicInts")(NonDeterministicInts)


def non_deterministic_ints_eager_fallback(shape, dtype=_dtypes.int64, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function non_deterministic_ints
  """
  _ctx = ctx if ctx else _context.context()
  if dtype is None:
    dtype = _dtypes.int64
  dtype = _execute.make_type(dtype, "dtype")
  _attr_shape_dtype, (shape,) = _execute.args_to_matching_eager([shape], _ctx, _dtypes.int64)
  _inputs_flat = [shape]
  _attrs = ("dtype", dtype, "shape_dtype", _attr_shape_dtype)
  _result = _execute.execute(b"NonDeterministicInts", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "NonDeterministicInts", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def rng_skip(resource, algorithm, delta, name=None):
  r"""Advance the counter of a counter-based RNG.

  The state of the RNG after
  `rng_skip(n)` will be the same as that after `stateful_uniform([n])`
  (or any other distribution). The actual increment added to the
  counter is an unspecified implementation detail.

  Args:
    resource: A `Tensor` of type `resource`.
      The handle of the resource variable that stores the state of the RNG.
    algorithm: A `Tensor` of type `int64`. The RNG algorithm.
    delta: A `Tensor` of type `int64`. The amount of advancement.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name, "RngSkip",
        name, _ctx._post_execution_callbacks, resource, algorithm, delta)
      return _result
    except _core._FallbackException:
      try:
        return rng_skip_eager_fallback(
            resource, algorithm, delta, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "RngSkip", resource=resource, algorithm=algorithm, delta=delta,
                   name=name)
  return _op
  _result = None
  return _result

def RngSkip(resource, algorithm, delta, name=None):
  return rng_skip(resource=resource, algorithm=algorithm, delta=delta, name=name)
RngSkip.__doc__ = rng_skip.__doc__
RngSkip = _doc_controls.do_not_generate_docs(_kwarg_only(RngSkip))
tf_export("raw_ops.RngSkip")(RngSkip)


def rng_skip_eager_fallback(resource, algorithm, delta, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function rng_skip
  """
  _ctx = ctx if ctx else _context.context()
  resource = _ops.convert_to_tensor(resource, _dtypes.resource)
  algorithm = _ops.convert_to_tensor(algorithm, _dtypes.int64)
  delta = _ops.convert_to_tensor(delta, _dtypes.int64)
  _inputs_flat = [resource, algorithm, delta]
  _attrs = None
  _result = _execute.execute(b"RngSkip", 0, inputs=_inputs_flat, attrs=_attrs,
                             ctx=_ctx, name=name)
  _result = None
  return _result


def stateful_random_binomial(resource, algorithm, shape, counts, probs, dtype=_dtypes.int64, name=None):
  r"""TODO: add doc.

  Args:
    resource: A `Tensor` of type `resource`.
    algorithm: A `Tensor` of type `int64`.
    shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
    counts: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`.
    probs: A `Tensor`. Must have the same type as `counts`.
    dtype: An optional `tf.DType` from: `tf.half, tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.int64`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `dtype`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "StatefulRandomBinomial", name, _ctx._post_execution_callbacks,
        resource, algorithm, shape, counts, probs, "dtype", dtype)
      return _result
    except _core._FallbackException:
      try:
        return stateful_random_binomial_eager_fallback(
            resource, algorithm, shape, counts, probs, dtype=dtype, name=name,
            ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  if dtype is None:
    dtype = _dtypes.int64
  dtype = _execute.make_type(dtype, "dtype")
  _, _, _op = _op_def_lib._apply_op_helper(
        "StatefulRandomBinomial", resource=resource, algorithm=algorithm,
                                  shape=shape, counts=counts, probs=probs,
                                  dtype=dtype, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("S", _op.get_attr("S"), "T", _op.get_attr("T"), "dtype",
            _op.get_attr("dtype"))
  _execute.record_gradient(
      "StatefulRandomBinomial", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def StatefulRandomBinomial(resource, algorithm, shape, counts, probs, dtype=_dtypes.int64, name=None):
  return stateful_random_binomial(resource=resource, algorithm=algorithm, shape=shape, counts=counts, probs=probs, dtype=dtype, name=name)
StatefulRandomBinomial.__doc__ = stateful_random_binomial.__doc__
StatefulRandomBinomial = _doc_controls.do_not_generate_docs(_kwarg_only(StatefulRandomBinomial))
tf_export("raw_ops.StatefulRandomBinomial")(StatefulRandomBinomial)


def stateful_random_binomial_eager_fallback(resource, algorithm, shape, counts, probs, dtype=_dtypes.int64, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function stateful_random_binomial
  """
  _ctx = ctx if ctx else _context.context()
  if dtype is None:
    dtype = _dtypes.int64
  dtype = _execute.make_type(dtype, "dtype")
  _attr_S, (shape,) = _execute.args_to_matching_eager([shape], _ctx)
  _attr_T, _inputs_T = _execute.args_to_matching_eager([counts, probs], _ctx, _dtypes.float64)
  (counts, probs) = _inputs_T
  resource = _ops.convert_to_tensor(resource, _dtypes.resource)
  algorithm = _ops.convert_to_tensor(algorithm, _dtypes.int64)
  _inputs_flat = [resource, algorithm, shape, counts, probs]
  _attrs = ("S", _attr_S, "T", _attr_T, "dtype", dtype)
  _result = _execute.execute(b"StatefulRandomBinomial", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "StatefulRandomBinomial", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def stateful_standard_normal(resource, shape, dtype=_dtypes.float32, name=None):
  r"""Outputs random values from a normal distribution. This op is deprecated in favor of op 'StatefulStandardNormalV2'

  The generated values will have mean 0 and standard deviation 1.

  Args:
    resource: A `Tensor` of type `resource`.
      The handle of the resource variable that stores the state of the RNG.
    shape: A `Tensor`. The shape of the output tensor.
    dtype: An optional `tf.DType`. Defaults to `tf.float32`.
      The type of the output.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `dtype`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "StatefulStandardNormal", name, _ctx._post_execution_callbacks,
        resource, shape, "dtype", dtype)
      return _result
    except _core._FallbackException:
      try:
        return stateful_standard_normal_eager_fallback(
            resource, shape, dtype=dtype, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  if dtype is None:
    dtype = _dtypes.float32
  dtype = _execute.make_type(dtype, "dtype")
  _, _, _op = _op_def_lib._apply_op_helper(
        "StatefulStandardNormal", resource=resource, shape=shape, dtype=dtype,
                                  name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("dtype", _op.get_attr("dtype"), "shape_dtype",
            _op.get_attr("shape_dtype"))
  _execute.record_gradient(
      "StatefulStandardNormal", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def StatefulStandardNormal(resource, shape, dtype=_dtypes.float32, name=None):
  return stateful_standard_normal(resource=resource, shape=shape, dtype=dtype, name=name)
StatefulStandardNormal.__doc__ = stateful_standard_normal.__doc__
StatefulStandardNormal = _doc_controls.do_not_generate_docs(_kwarg_only(StatefulStandardNormal))
tf_export("raw_ops.StatefulStandardNormal")(StatefulStandardNormal)


def stateful_standard_normal_eager_fallback(resource, shape, dtype=_dtypes.float32, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function stateful_standard_normal
  """
  _ctx = ctx if ctx else _context.context()
  if dtype is None:
    dtype = _dtypes.float32
  dtype = _execute.make_type(dtype, "dtype")
  _attr_shape_dtype, (shape,) = _execute.args_to_matching_eager([shape], _ctx, _dtypes.int64)
  resource = _ops.convert_to_tensor(resource, _dtypes.resource)
  _inputs_flat = [resource, shape]
  _attrs = ("dtype", dtype, "shape_dtype", _attr_shape_dtype)
  _result = _execute.execute(b"StatefulStandardNormal", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "StatefulStandardNormal", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def stateful_standard_normal_v2(resource, algorithm, shape, dtype=_dtypes.float32, name=None):
  r"""Outputs random values from a normal distribution.

  The generated values will have mean 0 and standard deviation 1.

  Args:
    resource: A `Tensor` of type `resource`.
      The handle of the resource variable that stores the state of the RNG.
    algorithm: A `Tensor` of type `int64`. The RNG algorithm.
    shape: A `Tensor`. The shape of the output tensor.
    dtype: An optional `tf.DType`. Defaults to `tf.float32`.
      The type of the output.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `dtype`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "StatefulStandardNormalV2", name, _ctx._post_execution_callbacks,
        resource, algorithm, shape, "dtype", dtype)
      return _result
    except _core._FallbackException:
      try:
        return stateful_standard_normal_v2_eager_fallback(
            resource, algorithm, shape, dtype=dtype, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  if dtype is None:
    dtype = _dtypes.float32
  dtype = _execute.make_type(dtype, "dtype")
  _, _, _op = _op_def_lib._apply_op_helper(
        "StatefulStandardNormalV2", resource=resource, algorithm=algorithm,
                                    shape=shape, dtype=dtype, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("dtype", _op.get_attr("dtype"), "shape_dtype",
            _op.get_attr("shape_dtype"))
  _execute.record_gradient(
      "StatefulStandardNormalV2", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def StatefulStandardNormalV2(resource, algorithm, shape, dtype=_dtypes.float32, name=None):
  return stateful_standard_normal_v2(resource=resource, algorithm=algorithm, shape=shape, dtype=dtype, name=name)
StatefulStandardNormalV2.__doc__ = stateful_standard_normal_v2.__doc__
StatefulStandardNormalV2 = _doc_controls.do_not_generate_docs(_kwarg_only(StatefulStandardNormalV2))
tf_export("raw_ops.StatefulStandardNormalV2")(StatefulStandardNormalV2)


def stateful_standard_normal_v2_eager_fallback(resource, algorithm, shape, dtype=_dtypes.float32, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function stateful_standard_normal_v2
  """
  _ctx = ctx if ctx else _context.context()
  if dtype is None:
    dtype = _dtypes.float32
  dtype = _execute.make_type(dtype, "dtype")
  _attr_shape_dtype, (shape,) = _execute.args_to_matching_eager([shape], _ctx, _dtypes.int64)
  resource = _ops.convert_to_tensor(resource, _dtypes.resource)
  algorithm = _ops.convert_to_tensor(algorithm, _dtypes.int64)
  _inputs_flat = [resource, algorithm, shape]
  _attrs = ("dtype", dtype, "shape_dtype", _attr_shape_dtype)
  _result = _execute.execute(b"StatefulStandardNormalV2", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "StatefulStandardNormalV2", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def stateful_truncated_normal(resource, algorithm, shape, dtype=_dtypes.float32, name=None):
  r"""Outputs random values from a truncated normal distribution.

  The generated values follow a normal distribution with mean 0 and standard
  deviation 1, except that values whose magnitude is more than 2 standard
  deviations from the mean are dropped and re-picked.

  Args:
    resource: A `Tensor` of type `resource`.
      The handle of the resource variable that stores the state of the RNG.
    algorithm: A `Tensor` of type `int64`. The RNG algorithm.
    shape: A `Tensor`. The shape of the output tensor.
    dtype: An optional `tf.DType`. Defaults to `tf.float32`.
      The type of the output.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `dtype`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "StatefulTruncatedNormal", name, _ctx._post_execution_callbacks,
        resource, algorithm, shape, "dtype", dtype)
      return _result
    except _core._FallbackException:
      try:
        return stateful_truncated_normal_eager_fallback(
            resource, algorithm, shape, dtype=dtype, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  if dtype is None:
    dtype = _dtypes.float32
  dtype = _execute.make_type(dtype, "dtype")
  _, _, _op = _op_def_lib._apply_op_helper(
        "StatefulTruncatedNormal", resource=resource, algorithm=algorithm,
                                   shape=shape, dtype=dtype, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("dtype", _op.get_attr("dtype"), "shape_dtype",
            _op.get_attr("shape_dtype"))
  _execute.record_gradient(
      "StatefulTruncatedNormal", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def StatefulTruncatedNormal(resource, algorithm, shape, dtype=_dtypes.float32, name=None):
  return stateful_truncated_normal(resource=resource, algorithm=algorithm, shape=shape, dtype=dtype, name=name)
StatefulTruncatedNormal.__doc__ = stateful_truncated_normal.__doc__
StatefulTruncatedNormal = _doc_controls.do_not_generate_docs(_kwarg_only(StatefulTruncatedNormal))
tf_export("raw_ops.StatefulTruncatedNormal")(StatefulTruncatedNormal)


def stateful_truncated_normal_eager_fallback(resource, algorithm, shape, dtype=_dtypes.float32, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function stateful_truncated_normal
  """
  _ctx = ctx if ctx else _context.context()
  if dtype is None:
    dtype = _dtypes.float32
  dtype = _execute.make_type(dtype, "dtype")
  _attr_shape_dtype, (shape,) = _execute.args_to_matching_eager([shape], _ctx, _dtypes.int64)
  resource = _ops.convert_to_tensor(resource, _dtypes.resource)
  algorithm = _ops.convert_to_tensor(algorithm, _dtypes.int64)
  _inputs_flat = [resource, algorithm, shape]
  _attrs = ("dtype", dtype, "shape_dtype", _attr_shape_dtype)
  _result = _execute.execute(b"StatefulTruncatedNormal", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "StatefulTruncatedNormal", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def stateful_uniform(resource, algorithm, shape, dtype=_dtypes.float32, name=None):
  r"""Outputs random values from a uniform distribution.

  The generated values follow a uniform distribution in the range `[0, 1)`. The
  lower bound 0 is included in the range, while the upper bound 1 is excluded.

  Args:
    resource: A `Tensor` of type `resource`.
      The handle of the resource variable that stores the state of the RNG.
    algorithm: A `Tensor` of type `int64`. The RNG algorithm.
    shape: A `Tensor`. The shape of the output tensor.
    dtype: An optional `tf.DType`. Defaults to `tf.float32`.
      The type of the output.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `dtype`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "StatefulUniform", name, _ctx._post_execution_callbacks, resource,
        algorithm, shape, "dtype", dtype)
      return _result
    except _core._FallbackException:
      try:
        return stateful_uniform_eager_fallback(
            resource, algorithm, shape, dtype=dtype, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  if dtype is None:
    dtype = _dtypes.float32
  dtype = _execute.make_type(dtype, "dtype")
  _, _, _op = _op_def_lib._apply_op_helper(
        "StatefulUniform", resource=resource, algorithm=algorithm,
                           shape=shape, dtype=dtype, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("dtype", _op.get_attr("dtype"), "shape_dtype",
            _op.get_attr("shape_dtype"))
  _execute.record_gradient(
      "StatefulUniform", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def StatefulUniform(resource, algorithm, shape, dtype=_dtypes.float32, name=None):
  return stateful_uniform(resource=resource, algorithm=algorithm, shape=shape, dtype=dtype, name=name)
StatefulUniform.__doc__ = stateful_uniform.__doc__
StatefulUniform = _doc_controls.do_not_generate_docs(_kwarg_only(StatefulUniform))
tf_export("raw_ops.StatefulUniform")(StatefulUniform)


def stateful_uniform_eager_fallback(resource, algorithm, shape, dtype=_dtypes.float32, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function stateful_uniform
  """
  _ctx = ctx if ctx else _context.context()
  if dtype is None:
    dtype = _dtypes.float32
  dtype = _execute.make_type(dtype, "dtype")
  _attr_shape_dtype, (shape,) = _execute.args_to_matching_eager([shape], _ctx, _dtypes.int64)
  resource = _ops.convert_to_tensor(resource, _dtypes.resource)
  algorithm = _ops.convert_to_tensor(algorithm, _dtypes.int64)
  _inputs_flat = [resource, algorithm, shape]
  _attrs = ("dtype", dtype, "shape_dtype", _attr_shape_dtype)
  _result = _execute.execute(b"StatefulUniform", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "StatefulUniform", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def stateful_uniform_full_int(resource, algorithm, shape, dtype=_dtypes.uint64, name=None):
  r"""Outputs random integers from a uniform distribution.

  The generated values are uniform integers covering the whole range of `dtype`.

  Args:
    resource: A `Tensor` of type `resource`.
      The handle of the resource variable that stores the state of the RNG.
    algorithm: A `Tensor` of type `int64`. The RNG algorithm.
    shape: A `Tensor`. The shape of the output tensor.
    dtype: An optional `tf.DType`. Defaults to `tf.uint64`.
      The type of the output.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `dtype`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "StatefulUniformFullInt", name, _ctx._post_execution_callbacks,
        resource, algorithm, shape, "dtype", dtype)
      return _result
    except _core._FallbackException:
      try:
        return stateful_uniform_full_int_eager_fallback(
            resource, algorithm, shape, dtype=dtype, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  if dtype is None:
    dtype = _dtypes.uint64
  dtype = _execute.make_type(dtype, "dtype")
  _, _, _op = _op_def_lib._apply_op_helper(
        "StatefulUniformFullInt", resource=resource, algorithm=algorithm,
                                  shape=shape, dtype=dtype, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("dtype", _op.get_attr("dtype"), "shape_dtype",
            _op.get_attr("shape_dtype"))
  _execute.record_gradient(
      "StatefulUniformFullInt", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def StatefulUniformFullInt(resource, algorithm, shape, dtype=_dtypes.uint64, name=None):
  return stateful_uniform_full_int(resource=resource, algorithm=algorithm, shape=shape, dtype=dtype, name=name)
StatefulUniformFullInt.__doc__ = stateful_uniform_full_int.__doc__
StatefulUniformFullInt = _doc_controls.do_not_generate_docs(_kwarg_only(StatefulUniformFullInt))
tf_export("raw_ops.StatefulUniformFullInt")(StatefulUniformFullInt)


def stateful_uniform_full_int_eager_fallback(resource, algorithm, shape, dtype=_dtypes.uint64, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function stateful_uniform_full_int
  """
  _ctx = ctx if ctx else _context.context()
  if dtype is None:
    dtype = _dtypes.uint64
  dtype = _execute.make_type(dtype, "dtype")
  _attr_shape_dtype, (shape,) = _execute.args_to_matching_eager([shape], _ctx, _dtypes.int64)
  resource = _ops.convert_to_tensor(resource, _dtypes.resource)
  algorithm = _ops.convert_to_tensor(algorithm, _dtypes.int64)
  _inputs_flat = [resource, algorithm, shape]
  _attrs = ("dtype", dtype, "shape_dtype", _attr_shape_dtype)
  _result = _execute.execute(b"StatefulUniformFullInt", 1,
                             inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                             name=name)
  _execute.record_gradient(
      "StatefulUniformFullInt", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def stateful_uniform_int(resource, algorithm, shape, minval, maxval, name=None):
  r"""Outputs random integers from a uniform distribution.

  The generated values are uniform integers in the range `[minval, maxval)`.
  The lower bound `minval` is included in the range, while the upper bound
  `maxval` is excluded.

  The random integers are slightly biased unless `maxval - minval` is an exact
  power of two.  The bias is small for values of `maxval - minval` significantly
  smaller than the range of the output (either `2^32` or `2^64`).

  Args:
    resource: A `Tensor` of type `resource`.
      The handle of the resource variable that stores the state of the RNG.
    algorithm: A `Tensor` of type `int64`. The RNG algorithm.
    shape: A `Tensor`. The shape of the output tensor.
    minval: A `Tensor`. Minimum value (inclusive, scalar).
    maxval: A `Tensor`. Must have the same type as `minval`.
      Maximum value (exclusive, scalar).
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `minval`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "StatefulUniformInt", name, _ctx._post_execution_callbacks, resource,
        algorithm, shape, minval, maxval)
      return _result
    except _core._FallbackException:
      try:
        return stateful_uniform_int_eager_fallback(
            resource, algorithm, shape, minval, maxval, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "StatefulUniformInt", resource=resource, algorithm=algorithm,
                              shape=shape, minval=minval, maxval=maxval,
                              name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("dtype", _op.get_attr("dtype"), "shape_dtype",
            _op.get_attr("shape_dtype"))
  _execute.record_gradient(
      "StatefulUniformInt", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def StatefulUniformInt(resource, algorithm, shape, minval, maxval, name=None):
  return stateful_uniform_int(resource=resource, algorithm=algorithm, shape=shape, minval=minval, maxval=maxval, name=name)
StatefulUniformInt.__doc__ = stateful_uniform_int.__doc__
StatefulUniformInt = _doc_controls.do_not_generate_docs(_kwarg_only(StatefulUniformInt))
tf_export("raw_ops.StatefulUniformInt")(StatefulUniformInt)


def stateful_uniform_int_eager_fallback(resource, algorithm, shape, minval, maxval, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function stateful_uniform_int
  """
  _ctx = ctx if ctx else _context.context()
  _attr_dtype, _inputs_dtype = _execute.args_to_matching_eager([minval, maxval], _ctx, _dtypes.int64)
  (minval, maxval) = _inputs_dtype
  _attr_shape_dtype, (shape,) = _execute.args_to_matching_eager([shape], _ctx, _dtypes.int64)
  resource = _ops.convert_to_tensor(resource, _dtypes.resource)
  algorithm = _ops.convert_to_tensor(algorithm, _dtypes.int64)
  _inputs_flat = [resource, algorithm, shape, minval, maxval]
  _attrs = ("dtype", _attr_dtype, "shape_dtype", _attr_shape_dtype)
  _result = _execute.execute(b"StatefulUniformInt", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "StatefulUniformInt", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def _InitOpDefLibrary(op_list_proto_bytes):
  op_list = _op_def_pb2.OpList()
  op_list.ParseFromString(op_list_proto_bytes)
  _op_def_registry.register_op_list(op_list)
  op_def_lib = _op_def_library.OpDefLibrary()
  op_def_lib.add_op_list(op_list)
  return op_def_lib
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# op {
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#     type_attr: "dtype"
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#   attr {
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#         type: DT_INT32
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#   attr {
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#   }
#   is_stateful: true
# }
# op {
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#   input_arg {
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#   input_arg {
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#     default_value {
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#     }
#   }
#   deprecation {
#     version: 29
#     explanation: "Use StatefulStandardNormalV2 instead"
#   }
#   is_stateful: true
# }
# op {
#   name: "StatefulStandardNormalV2"
#   input_arg {
#     name: "resource"
#     type: DT_RESOURCE
#   }
#   input_arg {
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#     type: DT_INT64
#   }
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# op {
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#   is_stateful: true
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# op {
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#   input_arg {
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# op {
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_op_def_lib = _InitOpDefLibrary(b"\nl\n\024NonDeterministicInts\022\024\n\005shape\"\013shape_dtype\032\017\n\006output\"\005dtype\"\021\n\005dtype\022\004type\032\0020\t\"\027\n\013shape_dtype\022\004type\032\0020\t\210\001\001\n4\n\007RngSkip\022\014\n\010resource\030\024\022\r\n\talgorithm\030\t\022\t\n\005delta\030\t\210\001\001\n\271\001\n\026StatefulRandomBinomial\022\014\n\010resource\030\024\022\r\n\talgorithm\030\t\022\n\n\005shape\"\001S\022\013\n\006counts\"\001T\022\n\n\005probs\"\001T\032\017\n\006output\"\005dtype\"\021\n\001S\022\004type:\006\n\0042\002\003\t\"\030\n\001T\022\004type\032\0020\002:\t\n\0072\005\023\001\002\003\t\"\034\n\005dtype\022\004type\032\0020\t:\t\n\0072\005\023\001\002\003\t\210\001\001\n\246\001\n\026StatefulStandardNormal\022\014\n\010resource\030\024\022\024\n\005shape\"\013shape_dtype\032\017\n\006output\"\005dtype\"\021\n\005dtype\022\004type\032\0020\001\"\027\n\013shape_dtype\022\004type\032\0020\tB(\010\035\022$Use StatefulStandardNormalV2 instead\210\001\001\n\215\001\n\030StatefulStandardNormalV2\022\014\n\010resource\030\024\022\r\n\talgorithm\030\t\022\024\n\005shape\"\013shape_dtype\032\017\n\006output\"\005dtype\"\021\n\005dtype\022\004type\032\0020\001\"\027\n\013shape_dtype\022\004type\032\0020\t\210\001\001\n\214\001\n\027StatefulTruncatedNormal\022\014\n\010resource\030\024\022\r\n\talgorithm\030\t\022\024\n\005shape\"\013shape_dtype\032\017\n\006output\"\005dtype\"\021\n\005dtype\022\004type\032\0020\001\"\027\n\013shape_dtype\022\004type\032\0020\t\210\001\001\n\204\001\n\017StatefulUniform\022\014\n\010resource\030\024\022\r\n\talgorithm\030\t\022\024\n\005shape\"\013shape_dtype\032\017\n\006output\"\005dtype\"\021\n\005dtype\022\004type\032\0020\001\"\027\n\013shape_dtype\022\004type\032\0020\t\210\001\001\n\213\001\n\026StatefulUniformFullInt\022\014\n\010resource\030\024\022\r\n\talgorithm\030\t\022\024\n\005shape\"\013shape_dtype\032\017\n\006output\"\005dtype\"\021\n\005dtype\022\004type\032\0020\027\"\027\n\013shape_dtype\022\004type\032\0020\t\210\001\001\n\251\001\n\022StatefulUniformInt\022\014\n\010resource\030\024\022\r\n\talgorithm\030\t\022\024\n\005shape\"\013shape_dtype\022\017\n\006minval\"\005dtype\022\017\n\006maxval\"\005dtype\032\017\n\006output\"\005dtype\"\021\n\005dtype\022\004type\032\0020\t\"\027\n\013shape_dtype\022\004type\032\0020\t\210\001\001")